Triple
T7413049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Karlsruhe |
E171057
|
entity |
| Predicate | affiliation |
P10
|
FINISHED |
| Object |
CESAER
CESAER is a European association of leading universities of science and technology dedicated to advancing engineering education, research, and innovation.
|
E28802
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: CESAER | Statement: [University of Karlsruhe, affiliation, CESAER]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CESAER Context triple: [University of Karlsruhe, affiliation, CESAER]
-
A.
Cesca
Cesca is a feminine given name, commonly used as a short form of Francesca.
-
B.
Chéserex
Chéserex is a small Swiss municipality in the canton of Vaud, situated near the Jura Mountains above Lake Geneva.
-
C.
Ceos
Ceos is an ancient Greek island in the Aegean Sea, known in mythology and history for its religious cults, poets, and connections to various deities and heroes.
-
D.
Cellese
Cellese is a regional dialect of the Franco-Provençal language traditionally spoken in a specific area of the Franco-Provençal linguistic region.
-
E.
Canace
Canace is a figure in Greek mythology, traditionally known as a daughter of Aeolus and Enarete and associated with tragic love stories.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: CESAER Triple: [University of Karlsruhe, affiliation, CESAER]
Generated description
CESAER is a European association of leading universities of science and technology dedicated to advancing engineering education, research, and innovation.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: CESAER Target entity description: CESAER is a European association of leading universities of science and technology dedicated to advancing engineering education, research, and innovation.
-
A.
CESAER
chosen
CESAER is a European association of leading universities of science and technology that collaborates to advance engineering education, research, and innovation.
-
B.
Cesca
Cesca is a feminine given name, commonly used as a short form of Francesca.
-
C.
Chéserex
Chéserex is a small Swiss municipality in the canton of Vaud, situated near the Jura Mountains above Lake Geneva.
-
D.
Ceos
Ceos is an ancient Greek island in the Aegean Sea, known in mythology and history for its religious cults, poets, and connections to various deities and heroes.
-
E.
Cellese
Cellese is a regional dialect of the Franco-Provençal language traditionally spoken in a specific area of the Franco-Provençal linguistic region.
- F. None of above.
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69c68a618bdc81908d8018edadecd1a4 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f2c336308190932c14cec5eec25f |
completed | March 27, 2026, 9:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8112960d48190a9947146e62daa13 |
completed | March 28, 2026, 5:34 p.m. |
| NEDg | Description generation | batch_69c811f1fe3c8190a591186b19044b5f |
completed | March 28, 2026, 5:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c812c1e9d08190b0e7f052d87011b9 |
completed | March 28, 2026, 5:41 p.m. |
Created at: March 27, 2026, 3:11 p.m.